System and method for reducing reconstruction artifacts in computed tomography images

ABSTRACT

A system and method reduce the noise, and hence improve the usefulness, of computer tomography (CT) images that have been corrupted by metal-induced reconstruction artifacts. Metal-induced reconstruction artifacts in CT images are reduced by restoring crucial quantitative image information. The system does not fail when applied to sparsely-sampled and/or low-resolution projection data and is clinically viable in that it is a method that can be embodied in a practical, real-world system that can be used routinely in hospitals and medical clinics, and relies only on data that are available from standard medical CT scanners.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part and claims the benefit of thefiling date of Provisional Application Ser. No. 60/038,330 filed Feb.26, 1997, by Alan D. Kalvin for "System and Method for ReducingReconstruction Artifacts in Computed Tomography Images". The disclosureof Application Ser. No. 60/038,330 is incorporated herein by reference.

DESCRIPTION BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to the field of computer imageprocessing of computer tomography (CT) data and, more particularly, toreducing artifacts in computed tomography images arising from theprocess of reconstructing the images from projections.

2. Background Description

U.S. Pat. No. 5,416,815, which is incorporated here by reference in itsentirety, describes computer tomography (CT) systems and the method ofimage reconstruction from projections. X-ray CT images of objectscontaining metal are often corrupted by noise in the form of bloomingand streaking artifacts that radiate from the regions of the image wherethe metal is present. These artifacts are referred to as "metal-inducedreconstruction" artifacts, and the process of reducing their effect as"metal artifact reduction" (or MAR). Metal-induced artifacts severelylimit the clinical usefulness of CT images, both for diagnostic andtherapeutic purposes.

The prior art methods of MAR fall into two groups. In the first groupare image processing methods which have been applied directly to thenoisy CT images. See, for example, D. D. Robertson, P. J. Weiss, E. K.Fishman, D. Magid, and P. S. Walker, "Evaluation of CT techniques forreducing artifacts in the presence of metallic orthopedic implants",Journal of Computer Assisted Tomography, March-April 1988, 12(2), pp.236-41; Hamid Soltanian-Zadeh, Joe P. Windham, and Jalal Soltanianzadeh,"CT Artifact Correction: An Image Processing Approach", SPIE MedicalImaging '96, Newport Beach, Calif., February 1996; and Heang K. Tuy, "AnAlgorithm to Reduce Clip Artifacts in CT Images", SPIE Vol. 1652 MedicalImaging VI: Image Processing (1992).

Methods which apply image processing directly, process the corrupted CTimages data only. These methods do not make use of any projection data.This approach is limited by the fact that essential image information iscompletely erased by MAR artifacts. This information cannot be recoveredsolely from the corrupted images themselves. Therefore, these methodsare unable to recover this information. Further, they do not directlyaddress the problem of recovering quantitative boundary information, butrather they focus on improving the overall qualitative appearance of theimages.

With the second group of methods, the projection data are processeddirectly (typically by filling in "missing" data), and the imagesreconstructed from these modified projections. See, for example, G. H.Glover and N. J. Pelc, "An algorithm for the reduction of metal clipartifacts in CT reconstructions", Medical Physics, 8(6),November/December 1981, pp. 799-807; T. Hinderling, P. Ruegsegger, M.Anliker, and C. Dietschi, "Computed Tomography reconstruction fromhollow projections: an application to in vivo evaluation of artificialhip joints", Journal of Computer Assisted Tomography, February 1979,3(1), pp. 52-57; W. A. Kalender, R. Hebel, and J. Ebersberger,"Reduction of CT artifacts caused by metallic implants", Radiology,August 1987, 164(2), pp. 57-7; E. Klotz, W. A. Kalender, R. Sokiranski,and D. Felsenberg, "Algorithms for the reduction of CT artifacts causedby metallic implants", Medical Imaging IV: PACS System Design andEvaluation, vol. 1234, Newport Beach, Calif., February 1990, pp.642-650; R. M. Lewitt and R. H. T. Bates, "Image reconstruction fromprojections: IV: Projection completion methods (computationalexamples)", Optik 50, 1978, pp. 269-278; B. E. Oppenheim,"Reconstruction tomography from incomplete projections", ReconstructionTomography in Diagnostic and Nuclear Medicine, Ter-Pogossian (editor),University Park Press, Baltimore, 1977, pp. 155-183; and G. Wang, D. L.Snyder, A. O'Sullivan, and M. W. Vannier, "Iterative deblurring for CTmetal artifact reduction", IEEE Trans. Medical Imaging, October 1996,14(5), pp. 657-664.

Methods which process projection data directly, process the projectiondata only. They do not make use of the CT image data. Further, a majordeficiency of these methods that they work only with a very specifictype of projection data; that is, projection data that (i) have beenhighly-sampled, and (ii) are of high resolution. The methods will failif applied to sparsely-sampled or low-resolution projection data.

DEFINITIONS

The basic concepts described in the present invention are betterunderstood with review of the following definitions.

PIXEL: A picture element. The basic element in a two-dimensional digital(2D) picture.

IMAGE: A rectangular 2D digital picture. Each pixel in the image isidentified by a pair of integers (x,y), where x and y are the column androw locations of the pixel respectively. (We shall use the terms "slice"and "image" interchangeably in describing the present invention).

SLICE: See IMAGE.

IMAGE SEGMENTATION: The process of identifying objects of interest in animage.

SEGMENTED OBJECT: A object of interest in an image identified by theprocess of segmentation.

SCANNING SCENE: The region of physical space, and the physical objectswithin it, that are scanned by a CT scanner.

INTRINSIC SCENE OBJECT: An object that is an integral part of the scenebeing scanned. The CT scanner table (on which a patient lies duringscanning) is an example of an intrinsic scene object.

EXTRINSIC SCENE OBJECT: An extraneous object that is deliberately placedin the scanning scene. Typically, an extraneous object is used as an aidin determining the properties of intrinsic objects. For example, anobject placed in the scene for purposes of calibrating the CT scanner isan extrinsic scene object.

OBJECT CONSTRAINT: A constraint on image pixel values that is derivedfrom information about the physical properties of the scanned objects.For example, the X-ray attenuation coefficient of a physical materialcan be used to restrict the pixel values of objects made from thatmaterial.

PROJECTION CONSTRAINT: A constraint on image pixel values that isderived from information about the projection data and projectiongeometry.

IMAGE CONSTRAINT: A general term for a constraint (either object orimage constraint) on image pixel data.

IMAGE ENHANCEMENT: The process of improving the quality of a digitalimage by reducing image noise. In describing the present invention, weshall use the term specifically to refer to the reduction of noisecaused by metal-induced reconstruction artifacts.

ENHANCED IMAGE: An image produced by application of image enhancementtechniques.

IMAGE MASK: Given a digital image N, a corresponding mask image is animage with the same dimensions (i.e., the same number of rows andcolumns) such that each pixel value in the mask image is a code thatrepresents a method for modifying the corresponding pixel value in thegiven image N.

RAY: This refers to a single X-ray beam that travels from the CT scannerX-ray tube to a detector cell on the CT scanner detector array.

RAYSUM: This refers to the basic unit of projection data collected by aCT scanner. It is the value associated with a single X-ray beam. It is ameasure of the total attenuation of the beam ray as it travels from itssource through the object being scanned to the CT detector array.

DERIVATIVE SET OF PROJECTION DATA: Given a set of projection data P, aderivative set of projection data D is a set of projection data derivedfrom P that has less information content. Typical examples of sets ofderivative projection data are sets produced when P is sub-sampled, whenits resolution is reduced, or when it is otherwise filtered.

SCOUT IMAGE: A scout image is a 2D digital X-ray produced when a CTmachine scans a collection of objects while the X-ray tube is held in afixed position, and the CT table, together with the objects, is moved.

SUMMARY OF THE INVENTION

It is therefore an object of the present invention to provide a systemand method to reduce the noise, and hence improve the usefulness, ofcomputer tomography (CT) images that have been corrupted bymetal-induced reconstruction artifacts.

It is a further an object to provide a system and a method for reducingmetal-induced reconstruction artifacts in CT images.

According to the invention, there is provided a metal artifact reduction(MAR) system that (a) restores crucial quantitative image information;(b) is applicable to sparsely-sampled and/or low-resolution projectiondata; and (c) is clinically viable in that it is a method that can beembodied in a practical, real-world system that can be used routinely inhospitals and medical clinics, and relies only on data that areavailable from standard medical CT scanners.

More specifically, this invention consists of a technique for processingCT scans of objects (typically human medical patients) that have piecesof metal embedded within them, and an algorithm for reducing MARartifacts in the CT images produced by the CT scanner using thetechnique according to the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, aspects and advantages will be betterunderstood from the following detailed description of a preferredembodiment of the invention with reference to the drawings, in which:

FIG. 1 is a block diagram of a computer tomography (CT) system using thepresent invention;

FIG. 2 is a flow chart showing the overall logic of the computerimplemented process according to the invention;

FIG. 3 is a flow chart showing the steps of computing object constraintsin the process of FIG. 2;

FIG. 4 illustrates how an object constraint mask is created from anobject of interest in a CT slice;

FIG. 5 is a flow chart showing the steps of the method for reducingreconstruction artifacts for a single 2D slice;

FIG. 6 is a flow chart showing the steps of applying object constraintsin the process of artifact reduction; and

FIG. 7 is a flow chart showing the steps of applying projectionconstraints in the process of artifact reduction.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT OF THE INVENTION

Referring now to the drawings, and more particularly to FIG. 1, there isshown one preferred embodiment of the present invention that uses imageprocessing to reduce reconstruction artifacts in computer tomography(CT) images 160 produced by a computed tomography scanner 105. Aphysical object (typically a medical patient) 120 is scanned by the CTscanner 105, collecting projection data 170, from which is created a setof 2D CT images 160. This process of creating the images 160 is known asimage reconstruction from projections.

The CT images 160 contain cross-sectional details of the scene that wasscanned, which typically includes the patient 120, intrinsic sceneobjects such as the scanner table 140, and extrinsic scene objects 130.Extrinsic scene objects 130 are objects that are specifically insertedinto the scene to provide additional information to the artifactreduction process. Extrinsic scene objects 130 useful forartifact-reduction generally have two common properties: (a) they showup clearly in CT images, with well-defined and distinct boundaries, and(b) their brightness values (pixel grey-level values) in artifact-freeCT images are known.

The CT images 160 and some subset 190 of the related CT projection data170 are input to a computer 150 and, optionally, stored on an externalstorage medium 154. The computer 150 may be, for example, an IBM RS/6000workstation running AIX, IBM's version of the UNIX operating system, andthe external storage medium 154 may be, for example, a disk storagesystem connected to the computer. An artifact reduction program 200running on the computer 150 reads in the CT images 160 and projectiondata 190, and outputs the set of CT images with a reduced amount ofartifact 180.

FIG. 2 shows a flow chart of the artifact reduction program 200 shown inFIG. 1. Each 2D CT slice is sequentially processed as follows. When theprocess begins, as shown in the initialization block 201, N, the numberof the slice currently being processed, is set equal to 1. In the nextstep, shown in function block 210, slice number N is input. In functionblock 220, one or more mask images are computed, based on the physicalcharacteristics of the objects appearing in slice N. These masks arelater used to apply constraints on pixel values in the slice. In thenext step, shown in function block 230, image constraints (i.e. objectconstraints and projection constraints) are applied to slice N, toproduce an enhanced version of slice N with reduced artifacts.

The steps of function blocks 210, 220 and 230 are repeated until all theslices have been enhanced. This is accomplished, following functionblock 230, by incrementing slice number N by 1, in function block 240.Then, a determination is made in decision block 250 as to whether N isgreater than the total number of image slices. If N is less than orequal to the total number of slices, then the steps shown in functionblocks 210, 220 and 230 are repeated. If N is greater than the totalnumber of slices, then the process terminates, giving as output thecomplete set of enhanced 2D slices, as shown in function block 260.

FIG. 3 shows a flowchart of the method for computing the set of objectconstraint masks (function block 220 in FIG. 2). In function block 320of FIG. 3, slice N is segmented, that is, objects of interest in slice Nare identified, using known computer vision techniques (see, forexample, Computer Vision by Ballard and Brown, Prentice Hall, 1982). AllN-1 enhanced slices (numbered 1,2, . . . N-1) that have already beenproduced at this stage can optionally be used in the segmentation ofslice N, as indicated at input block 310. From the segmentation, objectconstraints for slice N are computed (function block 330).

As shown in function block 350, the set of object constraint masks forslice N is then created. All currently existing sets of objectconstraint masks (i.e. those for slices 1,2, . . . N-1) can optionallybe used in the creation of the set of masks for slice N, as indicated atinput block 340. In the current preferred embodiment, M, the number ofmasks produced for slice N, is equal to the number of objects ofinterest produced by the segmentation of slice N, i.e., one mask foreach object of interest.

In constructing these masks, we assume that the X-ray attenuationcoefficient of each object of interest K (K=1,2, . . . M) is known. Wecan do this since tables of attenuation coefficients of many materialshave been published. For example, all the attenuation coefficients thatare of interest to us appear in H. Hubbell and S. M. Seltzer, "Tables ofX-Ray Mass Attenuation Coefficients and Mass Energy-AbsorbtionCoefficients 1 keV to 20 MeV for elements Z=1 to 92 and 48 AdditionalSubstances of Dosimetric Interest", National Institute of Standards andTechnology report NISTR 5632, 1996. Alternatively, attenuationcoefficients can be empirically evaluated.

From these attenuation coefficients, we can determine the correctgrey-level (or density) value of the pixels in slice N belonging toobject K, and from this information we construct the K^(th) constraintmask as follows. For each slice pixel (x,y) belonging to object K, thematching mask pixel (x,y) is set equal to the correct pixel value forobject K. Let us call this value Q_(R). All other pixels in the mask areassigned a special "no-constraint" value indicating that no constraintsare imposed by object K on these pixels.

FIG. 4 illustrates an example of this process. Within slice N (item400), the K^(th) object of interest 410 is shown (the shaded region). Inthe K^(th) mask (item 420) for slice N, the value of each mask pixel(x,y) inside the matching shaded region 430 is set to Q_(K), the correctvalue for slice pixel (x,y). All other pixels are set to the"no-constraint" value. (Alternative embodiments of this method can usemore general constraints, for example ranges of valid values). Thesemasks are later used to apply specific constraints during the imageenhancement process (function block 540 of FIG. 5, described infra).

In FIG. 5, the method for reducing reconstruction artifacts isillustrated. In function block 530, an initial enhanced version of sliceN, input from block 510, is created from slice N and, optionally fromblock 520 (i.e. from the existing N-1 enhanced slices numbered 1,2, . .. N-1). If the optional data from input block 520 are not used, theenhanced slice N is initialized by setting it equal to the originalslice N (block 510). If the data from block 520 are used, initializationis achieved by combining the original slice N data (block 510) with anextrapolation of the optional data, using the fact that slices 1,2, . .. , N are a set of sequential cross-sections of a 3D object.

The step shown in function block 540 is the first step in an iterativeprocess that is repeatedly executed until prescribed terminationcriteria have been met. Typically, criteria for termination are (a) theamount of change made to the enhanced slice in the previous iterationfall below a threshold, or (b) a maximum number of iterations of theloop is reached. In the step shown in function block 540, the set ofconstraint masks (block 550) is used to impose object constraints onslice N, which is modified accordingly. In function block 560, slice Nis again modified by checking it for consistency against the projectiondata, shown as input from block 570, and updating it to increase thedegree of consistency. (The methods applied in function blocks 540 and560 are described in more details infra.) A test is then made indecision block 580 to determine if any of the criteria for terminatingthis iterative loop (as described supra) have been met. If not, anotheriteration is begun by looping back to function block 540. When one ormore of the criteria for terminating the iterative loop have been met,the process terminates with the final version of enhanced slice N beingoutput in block 590.

FIG. 6 shows the steps involved in applying object constraints in theprocess of artifact reduction. In function block 600, M, the counter forthe constraint masks for slice N is initialized to 1. In block 610, thecounter for slice column is initialized to 1, and in block 620, thecounter for slice row is initialized to 1. In function block 630, pixel(x,y) in slice N is modified by the function F as follows. Let v be thecurrent value of pixel (x,y) in slice N. and let Q be the value of pixel(x,y) in mask M. If Q is the special "no-constraint" value, slice pixel(x,y) is not changed. Otherwise slice pixel (x,y) is set to Q. The ycounter is incremented by 1 (block 640) and if y is not greater thanY_(TOT) (the total number of rows in slice N) then the process infunction blocks 630 and 640 is repeated. Similarly, the x counter isincremented and tested in blocks 660 and 670, and if x is not greaterthan X_(TOT) (the total number of columns in slice N), the process infunction blocks 620, 630, 640, 650, and 660 is repeated. In the samemanner, the M counter is incremented and tested in blocks 680 and 690,and if M is not greater than M_(TOT) (the total number of constraintmasks for slice N), the process in function blocks 610, 620, 630, 640,650, 660, 670, and 680 is repeated. When M finally becomes greater thanM_(TOT), the process shown in FIG. 6 is complete.

FIG. 7 is a flow chart showing the steps of applying projectionconstraints in the process of artifact reduction. This part of theinvention involves iterating through all the projection rays associatedwith slice N. (We say that a set of rays are associated with slice N ifall the rays lie in the same 2D plane in the scanning scene as slice N.)In each iteration a subset of pixels in slice N is updated according tothe constraints imposed by the ray.

We make use of the following three definitions in describing the logicof the flowchart in FIG. 7. Given a ray R, the ray footprint, F_(R)={p₁,p₂, . . . p_(n) }, is the set of image pixels through which ray Rpasses. The footprint weights, W_(R) ={w₁,w₂, . . . w_(n) }, are definedso that w_(i) is the length of the intersection of ray R with footprintpixel p_(i). The two sets are computed using the geometry of theprojection data (i.e., the locations of the rays in the scanning scene),and the geometry of the slice, block 720 (i.e., the location in thescanning scene of the 2D cross-section depicted by slice N). We alsomake use of the term raysum as defined in the "Background of theInvention" section.

In function block 700 the counter R for projection rays is set toR_(first), the first ray to be used to impose projection constraints onslice N. In block 730, S is assigned the value of raysum R (obtainedfrom the projection data in input block 710). In function block 740, theray footprint F_(R) ={p₁,p₂, . . . p_(n) }, and the footprint weightsW_(R) ={w₁,w₂, . . . w_(n) }, are computed. In function block 750, E,the estimated ray sum, is computed as the weighted sum of the footprintpixels. The difference D between the true ray sum S, and E is alsocomputed. In function block 760, the projection constraints dictated byray R are applied, by updating each footprint pixel p_(i) (i=1,2, . . .,n) according to the formula shown. Decision block 770 checks ifR=R_(last), i.e. if we have used all the rays for slice N. If the answeris no, the next ray to be used is generated (block 780), and blocks730,740,750, and 760 are re-executed. If the answer to decision block isyes, the process shown in FIG. 7 is complete.

The function NEXT (block 780) can be any function which iterates throughall the rays, generating each ray exactly once. In this preferredembodiment we use the function

    NEXT(i)=i(MOD R.sub.total)+1,

where R_(total) is the total number of rays. It is interesting to notethat the technique for applying projection constraints that is describedin FIG. 7 is somewhat related to a general family of techniques known asalgebraic reconstruction techniques or ART, that are used forreconstructing images from projections, that is, used for creating 2Dimages from projection data. (See for example, Y. Censor, "FiniteSeries-Expansion Reconstruction Methods", Proceedings of the IEEE, vol.71, no.3, 1983, pp. 409-419). One basic, and crucial difference betweenthe process described in FIG. 7 and the ART methods is that the formeris just a single step in a multiplicity of steps that make up thepresent invention for image enhancement by artifact reduction. The ARTmethods on the other hand, are methods that solve a different problem,namely the problem of image reconstruction, and an ART method is not asingle step in an larger algorithm, but constitutes the entirereconstruction algorithm itself.

Given this disclosure, alternative equivalent embodiments will becomeapparent to those skilled in the art. These embodiments are also withinthe contemplation of the invention. Thus, while the invention has beendescribed in terms of a single preferred embodiment, those skilled inthe art will recognize that the invention can be practiced withmodification within the spirit and scope of the appended claims.

Having thus described our invention, what we claim as new and desire tosecure by letters patent is as follows:
 1. A computer implemented methodof processing computed tomography (CT) images comprising:a) providing amultiplicity of CT images, each of said images having a value, N; b)setting N=1; c) setting a total value for N=N_(TOT) ; d) computingconstraints on a multiplicity of pixel values for an image, N, to beprocessed; e) applying the constraints to reduce artifacts in saidimage, N; f) adding 1 to N; g) determining if N=N_(TOT) ; and h)repeating steps d) to g) until N=N_(TOT).
 2. A computer implementedmethod as in claim 1, wherein said step of computing constraints fromsaid objects classified as "objects of interest" comprises the step ofcreating a multiplicity of mask images from a digital input of an image.3. A computer implemented method as in claim 2, further comprising thestep of using a multiplicity of enhanced images, N, and a multiplicityof masks of these enhanced images, N, for creating a set of objectconstraint masks of from a digital input of an image.
 4. A computerimplemented method as in claim 3, wherein said step of processingprojection data uses a multiplicity of enhanced images, N, and amultiplicity of masks of enhanced images, N.
 5. A computer implementedmethod as in claim 1, wherein said step of applying an artifactreduction algorithm to said image, N further comprises the steps of:a)creating an initial version of an enhanced image, N, from image, N; b)setting a stopping criteria; c) applying object constraints to saidenhanced image, N; d) applying projection constraints, derived fromprojection data, to said enhanced image, N; e) determining if stoppingcriteria has been met; and f) repeating steps c) through e) until saidstopping criteria has been met.
 6. A computer system having one or morememories, one or more central processing units, one or more imagedisplay devices, the system further comprising:one or more databases ofprojection data, said projection data being one or more 2d data sets ofvalues wherein each said value is an estimation of a sum of X-rayattenuation coefficients of one or more objects along one of a pluralityof rays passing a collection of objects, one or more of the sums havingestimation errors greater than a tolerance; a pixel data set ofinformation that describes an image with noise, the data set beingproduced from the projection data, and the noise caused by theestimation errors; and a image enhancing process that iterativelychanges the pixel data set to reduce the noise by computing imageconstraints from (a) the image data, and (b) from a derivative of theprojection data, and using said constraints to modify the pixelinformation of one or more pixels during every iteration to create a newimage.
 7. A system, as in claim 6, wherein said rays emanate from a raysource and a view is a set of rays emanating from said ray source beingat a fixed point in space and said derivative projection data includeone or more of the views.
 8. A system, as in claim 7, wherein said raysemanate from a ray source and a view is a set of rays emanating fromsaid ray source being at a fixed point in space and said derivativeprojection data include one or more of said views from the fixed pointsin space, said views being separated by equal angular distances.
 9. Asystem, as in claim 6, wherein the quality of the said derivativeprojection data can be substantially lower than the quality than theoriginal projection data.
 10. A system, as in claim 6, wherein the saidderivative projection data are obtained from scout data.
 11. A system,as in claim 6, wherein the said collection of objects contains one ormore extrinsic objects that are inserted into the scanned scenespecifically for the purpose of creating additional image constraints.12. A system, as in claim 6, wherein one or more of the said constraintsare obtained from additional CT slices of the said collection ofobjects.